Machine Learning and AI

Description

We are part of an era that has self-drive cars, computer composed music, AI assistants on our fingertips and much more. We are in the future, which was once a fantasy. Where all labor, whether manual or intellectual, can be automated and optimized. Be it deep-tech, e-commerce, fin-tech, food-tech, healthcare and more. AI and ML are transforming every sector today boosting long term career scopes. But how does one start to comprehend and master the principles of machine learning, data science, and AI? From identifying a real-world business problem to arriving at it's working and deployable AI solution? Now that's where Foxmula comes in with its Intensive profile building program in Machine learning and AI. Our primary focus will be on getting you to build real-world AI solutions using the skills you will learn in this program. All the concepts, from fundamentals to the advance portions, will be taught in an intuitive fashion with tangible real-life examples. To make sure that you are an active learner in this process, we have practical implementation at every level of the program. This allows you to master the skills regardless of the background you come from.

Process


  • Instructor-led Training

    90 hours live weekend sessions | Batch of 30 trainees

  • International certifications
  • Industrial Exposure

    Live project with internship completion letter.

Curriculum

  • What is Data Science?
  • What does Data Science involve?
  • Era of Data Science
  • Business Intelligence vs Data Science
  • Life cycle of Data Science
  • Tools of Data Science
  • What is Statistical Inference?
  • Terminologies of Statistics
  • Measures of Centers
  • Measures of Spread
  • Bivariate Analysis
  • Multi variate analysis
  • Plots
  • Data Analysis Pipeline
  • What is Data Extraction
  • Types of Data
  • Raw and Processed Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Raw and Processed Data
  • Machine Learning Categories
  • Supervised Learning
  • Linear Regression
  • Logistic Regression
  • What is Classification and its use cases?
  • Decision Tree?
  • Algorithms for Decision Tree
  • Evaluation
  • Random Forest
  • Boosting algorithms
  • KNN
  • Support Vector Machine: Classification
  • Introduction
  • Linear Regression with One Variable
  • Linear Algebra Review
  • Linear Regression with Multiple Variables
  • Octave/Matlab Tutorial
  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • What is Hierarchical Clustering?
  • What is Association Rules & its use cases?
  • What is Recommendation Engine & it’s working?
  • Types of Recommendation Types
  • User-Based Recommendation
  • Item-Based Recommendation
  • Difference: User-Based and Item-Based Recommendation.
  • Recommendation Use-case
  • The concepts of text-mining
  • Use cases
  • Text cleaning
  • Text to features
  • Text Classification